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library(tidyverse)

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library(tayloRswift)
library(tidytuesdayR)
library(ggplot2)
library(dplyr)
library(ggthemes)
library(maps)

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library(sf)

## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

library(cartography)

## This project is in maintenance mode. 
## Core functionalities of `cartography` can be found in `mapsf`.
## https://riatelab.github.io/mapsf/

library(plotly)

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shapenames <- read_sf( "CA_Counties", "CA_Counties_TIGER2016")
jail_America <- readr::read_csv("./Allstatesinsurvey/all_jails.csv")

## Rows: 523 Columns: 116
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): statecode, state, state_notes, county, jail, jail_notes, med2008, ...
## dbl (98): id, fips, d2008, d2009, d2010, d2011, d2012, d2013, d2014, d2015, ...
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jail_deaths_in_Americas <- readr::read_csv("./Allstatesinsurvey/all_deaths.csv")

## Rows: 7571 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): state, county, jail, date_of_death, full_name, last_name, first_na...
## dbl  (4): id, year, yob, age
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#irports_Jordan %>%
  #leaflet() %>% 
  #addTiles() %>% 
  #addMarkers(lng = ~longitude, lat = ~latitude, label = ~description)
#calif

California_deaths <-jail_deaths_in_Americas %>% 
filter(state=="CA")%>%
group_by(county)%>%
summarize( total_deaths=n()) #counts how many rows inside that group


prisondeath<-shapenames %>% 
  left_join( California_deaths, by=c( "NAME"= "county"))
   
  map <-  ggplot(data= prisondeath, aes(fill= total_deaths)) + 
    geom_sf(fill="white")+
    labs(x="Latitude of California", y="Lonigitude of California")+
    geom_sf_text(mapping = aes(label= NAME), size=1)+
      theme(legend.position = "none")

ggplotly(map)